EFA
set.seed(42)
library(rcompanion) # effect size calculation
library(igraph)##
## Attaching package: 'igraph'## The following objects are masked from 'package:stats':
##
## decompose, spectrum## The following object is masked from 'package:base':
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## unionlibrary(corrplot)## corrplot 0.95 loadedlibrary(QuantPsyc) # for the multivariate normality test## Loading required package: boot## Loading required package: dplyr##
## Attaching package: 'dplyr'## The following objects are masked from 'package:igraph':
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## as_data_frame, groups, union## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union## Loading required package: purrr##
## Attaching package: 'purrr'## The following objects are masked from 'package:igraph':
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## compose, simplify## Loading required package: MASS##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
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## select##
## Attaching package: 'QuantPsyc'## The following object is masked from 'package:base':
##
## normlibrary(dunn.test)
library(nFactors) # for the scree plot## Loading required package: lattice##
## Attaching package: 'lattice'## The following object is masked from 'package:boot':
##
## melanoma##
## Attaching package: 'nFactors'## The following object is masked from 'package:lattice':
##
## parallellibrary(psych) # for PA FA##
## Attaching package: 'psych'## The following object is masked from 'package:boot':
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## logit## The following object is masked from 'package:rcompanion':
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## philibrary(caret) # highly correlated features removal## Loading required package: ggplot2##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
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## %+%, alpha##
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## ✖ tibble::as_data_frame() masks dplyr::as_data_frame(), igraph::as_data_frame()
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## ✖ tidyr::crossing() masks igraph::crossing()
## ✖ dplyr::filter() masks stats::filter()
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(paletteer) # color palettes
library(conflicted) # to resolve QuantPsyc x dplyr conflicts
conflict_prefer("select", "dplyr")## [conflicted] Will prefer dplyr::select over any other package.conflict_prefer("filter", "dplyr")## [conflicted] Will prefer dplyr::filter over any other package.# library(showtext)
# Add custom font
# font_add(
# "Noto Sans",
# "/usr/share/fonts/Noto_Sans/NotoSans-Regular.ttf"
# ) # Use the path to your .ttf file
# showtext_auto()
# noto_theme <- theme(text = element_text(family = "Noto Sans"))
noto_theme <- theme()Helpers
analyze_distributions <- function(data_factors_long, variable) {
factors <- levels(data_factors_long$factor)
print(table(data_factors_long[[variable]], useNA = "ifany") / length(factors))
plot_all <- data_factors_long %>%
ggplot(aes(x = factor_score, y = !!sym(variable))) +
geom_boxplot() +
facet_grid(factor ~ .) +
labs(x = "factor score")
print(plot_all)
plot <- data_factors_long %>%
drop_na(!!sym(variable)) %>%
ggplot(aes(x = factor_score, y = !!sym(variable))) +
geom_boxplot() +
facet_grid(factor ~ .) +
labs(x = "factor score") +
noto_theme +
theme_bw()
ggsave(paste(c("distr", variable, ".pdf"), collapse = ""))
ggsave(paste(c("distr", variable, ".png"), collapse = ""))
print(plot)
# formula <- reformulate(variable, "factor_score")
chi2 <- numeric()
p_val <- numeric()
epsilon2 <- numeric()
epsilon2_lci <- numeric()
epsilon2_uci <- numeric()
min_p_values <- numeric()
for (f in factors) {
data <- data_factors_long %>% filter(factor == f)
cat(
"\nTest for the significance of differences in",
variable, "over", f, ":\n\n"
)
kw <- kruskal.test(data$factor_score, data[[variable]])
dunn <- dunn.test(
data$factor_score, data[[variable]],
altp = TRUE, method = "bonferroni"
)
e2_test <- epsilonSquared(data$factor_score, data[[variable]], ci = TRUE)
e2 <- e2_test[[1]]
e2_lci <- e2_test[[2]]
e2_uci <- e2_test[[3]]
cat("epsilon2 = ", e2, "(95% CI:", e2_lci, "-", e2_uci, ")\n")
min_p_values <- c(min_p_values, min(dunn$altP.adjusted))
chi2 <- c(chi2, kw$statistic[[1]])
p_val <- c(p_val, kw$p.value)
epsilon2 <- c(epsilon2, e2)
epsilon2_lci <- c(epsilon2_lci, e2_lci)
epsilon2_uci <- c(epsilon2_uci, e2_uci)
}
cat("\n")
print(
data.frame(
factor = factors,
chi2 = chi2,
kruskal_p = p_val,
epsilon2_lci = epsilon2_lci,
epsilon2 = epsilon2,
epsilon2_uci = epsilon2_uci
) %>% mutate(
across(c(epsilon2, epsilon2_lci, epsilon2_uci), ~ round(.x, 3))
) %>%
mutate(across(kruskal_p, ~ case_when(
.x < 0.0001 ~ "< 0.0001",
.x < 0.001 ~ "< 0.001",
.x < 0.01 ~ "< 0.01",
.x < 0.05 ~ "< 0.05",
.default = as.character(round(.x, 2))
))) %>%
mutate(across(chi2, ~ round(.x, 2)))
)
cat(
"\np < 5e-2 found in:",
factors[min_p_values < 0.05],
"\np < 1e-2 found in:",
factors[min_p_values < 0.01],
"\np < 1e-3 found in:",
factors[min_p_values < 0.001],
"\np < 1e-4 found in:",
factors[min_p_values < 0.0001], "\n"
)
}
data_factor_bind <- function(data, fa_fit) {
data_factors <- bind_cols(data, fa_fit$scores %>% as.data.frame())
colnames(data_factors) <- prettify_feat_name_vector(colnames(data_factors))
fnames <- colnames(fa_fit$loadings)
data_factors_long <- data_factors %>%
pivot_longer(
any_of(fnames),
names_to = "factor", values_to = "factor_score"
) %>%
mutate(across(
factor,
~ factor(.x, levels = fnames)
)) %>%
select(
all_of(1:(.firstnonmetacolumn - 1)), factor, factor_score, everything()
)
data_factors_longer <- data_factors_long %>% pivot_longer(
all_of((.firstnonmetacolumn + 2):ncol(data_factors_long)),
names_to = "feat", values_to = "feat_value"
)
return(list(
data = data_factors,
long = data_factors_long,
feat_long = data_factors_longer
))
}Load and tidy data
pretty_names <- read_csv("../feat_name_mapping.csv")## Rows: 85 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): name_orig, name_pretty
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.prettify_feat_name <- function(x) {
name <- pull(pretty_names %>%
filter(name_orig == x), name_pretty)
if (length(name) == 1) {
return(name)
} else {
return(x)
}
}
prettify_feat_name_vector <- function(x) {
map(
x,
prettify_feat_name
) %>% unlist()
}
data <- read_csv("../measurements/measurements.csv")## Rows: 753 Columns: 108
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (20): fpath, KUK_ID, FileName, FileFormat, FolderPath, subcorpus, Source...
## dbl (85): RuleAbstractNouns, RuleAmbiguousRegards, RuleAnaphoricReferences, ...
## lgl (3): ClarityPursuit, SyllogismBased, Bindingness
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message..firstnonmetacolumn <- 17
data_no_nas <- data %>%
select(!c(
fpath,
# KUK_ID,
# FileName,
FolderPath,
# subcorpus,
DocumentTitle,
ClarityPursuit,
Readability,
SyllogismBased,
SourceDB
)) %>%
# replace -1s in variation coefficients with NAs
mutate(across(c(
`RuleDoubleAdpos.max_allowable_distance.v`,
`RuleTooManyNegations.max_negation_frac.v`,
`RuleTooManyNegations.max_allowable_negations.v`,
`RuleTooManyNominalConstructions.max_noun_frac.v`,
`RuleTooManyNominalConstructions.max_allowable_nouns.v`,
`RuleCaseRepetition.max_repetition_count.v`,
`RuleCaseRepetition.max_repetition_frac.v`,
`RulePredSubjDistance.max_distance.v`,
`RulePredObjDistance.max_distance.v`,
`RuleInfVerbDistance.max_distance.v`,
`RuleMultiPartVerbs.max_distance.v`,
`RuleLongSentences.max_length.v`,
`RulePredAtClauseBeginning.max_order.v`,
`mattr.v`,
`maentropy.v`
), ~ na_if(.x, -1))) %>%
# replace NAs with 0s
replace_na(list(
RuleGPcoordovs = 0,
RuleGPdeverbaddr = 0,
RuleGPpatinstr = 0,
RuleGPdeverbsubj = 0,
RuleGPadjective = 0,
RuleGPpatbenperson = 0,
RuleGPwordorder = 0,
RuleDoubleAdpos = 0,
RuleDoubleAdpos.max_allowable_distance.v = 0,
RuleAmbiguousRegards = 0,
RuleReflexivePassWithAnimSubj = 0,
RuleTooManyNegations = 0,
RuleTooManyNegations.max_negation_frac.v = 0,
RuleTooManyNegations.max_allowable_negations.v = 0,
RuleTooManyNominalConstructions.max_noun_frac.v = 0,
RuleTooManyNominalConstructions.max_allowable_nouns.v = 0,
RuleFunctionWordRepetition = 0,
RuleCaseRepetition.max_repetition_count.v = 0,
RuleCaseRepetition.max_repetition_frac.v = 0,
RuleWeakMeaningWords = 0,
RuleAbstractNouns = 0,
RuleRelativisticExpressions = 0,
RuleConfirmationExpressions = 0,
RuleRedundantExpressions = 0,
RuleTooLongExpressions = 0,
RuleAnaphoricReferences = 0,
RuleLiteraryStyle = 0,
RulePassive = 0,
RulePredSubjDistance = 0,
RulePredSubjDistance.max_distance.v = 0,
RulePredObjDistance = 0,
RulePredObjDistance.max_distance.v = 0,
RuleInfVerbDistance = 0,
RuleInfVerbDistance.max_distance.v = 0,
RuleMultiPartVerbs = 0,
RuleMultiPartVerbs.max_distance.v = 0,
RuleLongSentences.max_length.v = 0,
RulePredAtClauseBeginning.max_order.v = 0,
RuleVerbalNouns = 0,
RuleDoubleComparison = 0,
RuleWrongValencyCase = 0,
RuleWrongVerbonominalCase = 0,
RuleIncompleteConjunction = 0
)) %>%
# merge GPs
mutate(
GPs = RuleGPcoordovs +
RuleGPdeverbaddr +
RuleGPpatinstr +
RuleGPdeverbsubj +
RuleGPadjective +
RuleGPpatbenperson +
RuleGPwordorder
) %>%
select(!c(
RuleGPcoordovs,
RuleGPdeverbaddr,
RuleGPpatinstr,
RuleGPdeverbsubj,
RuleGPadjective,
RuleGPpatbenperson,
RuleGPwordorder
)) %>%
# norm data expected to correlate with text length
mutate(across(c(
GPs,
RuleDoubleAdpos,
RuleAmbiguousRegards,
RuleFunctionWordRepetition,
RuleWeakMeaningWords,
RuleAbstractNouns,
RuleRelativisticExpressions,
RuleConfirmationExpressions,
RuleRedundantExpressions,
RuleTooLongExpressions,
RuleAnaphoricReferences,
RuleLiteraryStyle,
RulePassive,
RuleVerbalNouns,
RuleDoubleComparison,
RuleWrongValencyCase,
RuleWrongVerbonominalCase,
RuleIncompleteConjunction,
num_hapax,
RuleReflexivePassWithAnimSubj,
RuleTooManyNominalConstructions,
RulePredSubjDistance,
RuleMultiPartVerbs,
RulePredAtClauseBeginning
), ~ .x / word_count)) %>%
mutate(across(c(
RuleTooFewVerbs,
RuleTooManyNegations,
RuleCaseRepetition,
RuleLongSentences,
RulePredObjDistance,
RuleInfVerbDistance
), ~ .x / sent_count)) %>%
# replace NAs with medians
mutate(across(c(
RuleDoubleAdpos.max_allowable_distance,
RuleTooManyNegations.max_negation_frac,
RuleTooManyNegations.max_allowable_negations,
RulePredSubjDistance.max_distance,
RulePredObjDistance.max_distance,
RuleInfVerbDistance.max_distance,
RuleMultiPartVerbs.max_distance
), ~ coalesce(., median(., na.rm = TRUE))))
data_clean <- data_no_nas %>%
# remove variables identified as text-length dependent
select(!c(
RuleTooFewVerbs,
RuleTooManyNegations,
RuleTooManyNominalConstructions,
RuleCaseRepetition,
RuleLongSentences,
RulePredAtClauseBeginning,
syllab_count,
char_count
)) %>%
# remove variables identified as unreliable
select(!c(
RuleAmbiguousRegards,
RuleFunctionWordRepetition,
RuleDoubleComparison,
RuleWrongValencyCase,
RuleWrongVerbonominalCase
)) %>%
# remove further variables belonging to the 'acceptability' category
select(!c(RuleIncompleteConjunction)) %>%
# remove artificially limited variables
select(!c(
RuleCaseRepetition.max_repetition_frac,
RuleCaseRepetition.max_repetition_frac.v
)) %>%
# remove variables with too many NAs
select(!c(
RuleDoubleAdpos.max_allowable_distance,
RuleDoubleAdpos.max_allowable_distance.v
)) %>%
mutate(across(c(
class,
FileFormat,
subcorpus,
DocumentVersion,
LegalActType,
Objectivity,
AuthorType,
RecipientType,
RecipientIndividuation,
Anonymized
), ~ as.factor(.x)))
# no NAs should be present now
data_clean[!complete.cases(data_clean[.firstnonmetacolumn:ncol(data_clean)]), ]## # A tibble: 0 × 77
## # ℹ 77 variables: KUK_ID <chr>, FileName <chr>, FileFormat <fct>,
## # subcorpus <fct>, SourceID <chr>, DocumentVersion <fct>,
## # ParentDocumentID <chr>, LegalActType <fct>, Objectivity <fct>,
## # Bindingness <lgl>, AuthorType <fct>, RecipientType <fct>,
## # RecipientIndividuation <fct>, Anonymized <fct>, Recipient Type <chr>,
## # class <fct>, RuleAbstractNouns <dbl>, RuleAnaphoricReferences <dbl>,
## # RuleCaseRepetition.max_repetition_count <dbl>, …colnames(data_clean) <- prettify_feat_name_vector(colnames(data_clean))Important features identification
feature_importances <- read_csv("../importance_measures/featcomp.csv")## Rows: 61 Columns: 21
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Variable, Sign
## dbl (15): Importance, p_value, estimate, wilcox_p, wilcox_r, kw_p, kw_chi2, ...
## lgl (4): selected_pval, wilcox_sel, kw_sel, selected_reg
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.selected_features_names <- feature_importances %>%
filter(kw_sel) %>%
pull(Variable)Correlations
See Levshina (2015: 353–54).
analyze_correlation <- function(data) {
cor_matrix <- cor(data)
cor_tibble_long <- cor_matrix %>%
as_tibble() %>%
mutate(feat1 = rownames(cor_matrix)) %>%
pivot_longer(!feat1, names_to = "feat2", values_to = "cor") %>%
mutate(abs_cor = abs(cor))
cor_matrix_upper <- cor_matrix
cor_matrix_upper[lower.tri(cor_matrix_upper)] <- 0
cor_tibble_long_upper <- cor_matrix_upper %>%
as_tibble() %>%
mutate(feat1 = rownames(cor_matrix)) %>%
pivot_longer(!feat1, names_to = "feat2", values_to = "cor") %>%
mutate(abs_cor = abs(cor)) %>%
filter(feat1 != feat2 & abs_cor > 0)
list(
cor_matrix = cor_matrix,
cor_matrix_upper = cor_matrix_upper,
cor_tibble_long = cor_tibble_long,
cor_tibble_long_upper = cor_tibble_long_upper
)
}
data_purish <- data_clean %>%
# remove readability metrics as they're conceptually different
# to the remaining features
select(!c(ari, cli, fkgl, fre, gf, smog)) %>%
# remove atl as it heavily reflects phenomena
# that cannot be influenced by the author
select(!atl) %>%
select(any_of(selected_features_names))High correlations
.hcorrcutoff <- 0.9
analyze_correlation(data_purish)$cor_tibble_long %>%
filter(feat1 != feat2 & abs_cor > .hcorrcutoff) %>%
arrange(feat1, -abs_cor) %>%
print(n = 100)## # A tibble: 4 × 4
## feat1 feat2 cor abs_cor
## <chr> <chr> <dbl> <dbl>
## 1 hpoint wordcount 0.958 0.958
## 2 maentropy mattr 0.964 0.964
## 3 mattr maentropy 0.964 0.964
## 4 wordcount hpoint 0.958 0.958high_correlations <- findCorrelation(
cor(data_purish),
verbose = TRUE, cutoff = .hcorrcutoff
)## Compare row 6 and column 5 with corr 0.958
## Means: 0.183 vs 0.183 so flagging column 5
## Compare row 19 and column 14 with corr 0.964
## Means: 0.17 vs 0.183 so flagging column 14
## All correlations <= 0.9names(data_purish)[high_correlations]## [1] "hpoint" "mattr"data_pureish_striphigh <- data_purish %>% select(!all_of(high_correlations))
analyze_correlation(data_pureish_striphigh)$cor_tibble_long %>%
filter(feat1 != feat2 & abs_cor > .hcorrcutoff) %>%
arrange(feat1, -abs_cor) %>%
print(n = 100)## # A tibble: 0 × 4
## # ℹ 4 variables: feat1 <chr>, feat2 <chr>, cor <dbl>, abs_cor <dbl>Low correlations
# 0.35 instead of 0.3 otherwise the FA bootstrapping would freeze
.lcorrcutoff <- 0.35
low_correlating_features <- analyze_correlation(data_pureish_striphigh)$
cor_tibble_long %>%
filter(feat1 != feat2) %>%
group_by(feat1) %>%
summarize(max_cor = max(abs_cor)) %>%
filter(max_cor < .lcorrcutoff) %>%
pull(feat1)
feature_importances %>%
filter(Variable %in% low_correlating_features) %>%
pull(Variable)## [1] "anaphoricrefs" "extrcaseexprs" "caserepcount.v"
## [4] "redundexprs" "relativisticexprs" "VERBcompdist.m"
## [7] "NOUNfrac.v" "verbalNOUNs" "abstractNOUNs"data_pure <- data_pureish_striphigh %>%
select(!any_of(low_correlating_features))
colnames(data_pure) <- prettify_feat_name_vector(colnames(data_pure))Visualisation
corrplot(cor(data_pure))
corrplot(abs(cor(data_pure)))
my_colors <- paletteer::paletteer_d("ggthemes::Classic_10_Medium")
network_edges <- analyze_correlation(data_pure)$cor_tibble_long_upper %>%
filter(abs_cor > .lcorrcutoff)
network <- graph_from_data_frame(
network_edges,
directed = FALSE
)
E(network)$weight <- network_edges$abs_cor
network_communities <- cluster_optimal(network)
network_membership <- membership(network_communities)
plot(
network,
layout = layout.fruchterman.reingold,
vertex.color = map(
network_communities$membership,
function(x) my_colors[x]
) %>% unlist(use.names = FALSE),
vertex.size = 6,
vertex.label.color = "black",
vertex.label.cex = 0.7
)
Scaling
data_scaled <- data_pure %>%
mutate(across(seq_along(data_pure), ~ scale(.x)[, 1]))Check for normality
mult.norm(data_scaled %>% as.data.frame())$mult.test## Beta-hat kappa p-val
## Skewness 1006.915 126367.8448 0
## Kurtosis 2532.745 457.9503 0mardia(data_scaled)
## Call: mardia(x = data_scaled)
##
## Mardia tests of multivariate skew and kurtosis
## Use describe(x) the to get univariate tests
## n.obs = 753 num.vars = 31
## b1p = 1006.92 skew = 126367.8 with probability <= 0
## small sample skew = 126902.9 with probability <= 0
## b2p = 2532.75 kurtosis = 457.95 with probability <= 0Low (null) p-values show that we can reject the hypothesis that the data would be in a multivariate normal distribution. I.e. the distribution isn’t multivariate normal.
Check for goodness of data
data_scaled %>%
cor() %>%
det()## [1] 1.192791e-10KMO(data_scaled)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = data_scaled)
## Overall MSA = 0.85
## MSA for each item =
## sentlen.m sentcount activity VERBfrac.m
## 0.90 0.72 0.90 0.86
## wordcount entropy sentlen.v predsubjdist.m
## 0.73 0.74 0.83 0.81
## compoundVERBs passives predobjdist.m literary
## 0.89 0.84 0.82 0.89
## verbdist maentropy predorder.m hapaxes
## 0.93 0.59 0.87 0.81
## VERBcomp NOUNcount.v subj NOUNcount.m
## 0.86 0.90 0.95 0.90
## predobjdist.v NEGcount.m compoundVERBsdist.m VERBfrac.v
## 0.91 0.71 0.83 0.81
## NEGcount.v compoundVERBsdist.v predsubjdist.v mamr
## 0.69 0.93 0.92 0.91
## obj predorder.v NEGfrac.m
## 0.69 0.87 0.65bartlett.test(data_scaled)##
## Bartlett test of homogeneity of variances
##
## data: data_scaled
## Bartlett's K-squared = 2.5035e-13, df = 30, p-value = 1Good and broad FA!
No. of vectors
fa_parallel_broad <- fa.parallel(data_scaled, fm = "pa", fa = "fa", n.iter = 20)
## Parallel analysis suggests that the number of factors = 7 and the number of components = NAfa_parallel_broad_df <- data.frame(
factor = seq_along(data_scaled),
actual = fa_parallel_broad$fa.values,
simulated = fa_parallel_broad$fa.sim
) %>%
pivot_longer(!factor, names_to = "data", values_to = "eigenvalue")
fa_parallel_broad_df %>%
ggplot(aes(x = factor, y = eigenvalue, linetype = data)) +
geom_line() +
geom_point(
data = fa_parallel_broad_df %>% filter(data == "actual"),
mapping = aes(x = factor, y = eigenvalue)
) +
labs(x = "factor number", y = "eigen values of principal factors") +
noto_theme +
theme_bw()
ggsave("scree.pdf", height = 4, width = 6)
ggsave("scree.png", height = 4, width = 6)Model
set.seed(42)
fa_broad <- fa(
data_scaled,
nfactors = 7,
fm = "pa",
rotate = "promax",
oblique.scores = TRUE,
scores = "tenBerge",
n.iter = 100
)## Loading required namespace: GPArotationfa_broad## Factor Analysis with confidence intervals using method = fa(r = data_scaled, nfactors = 7, n.iter = 100, rotate = "promax",
## scores = "tenBerge", fm = "pa", oblique.scores = TRUE)
## Factor Analysis using method = pa
## Call: fa(r = data_scaled, nfactors = 7, n.iter = 100, rotate = "promax",
## scores = "tenBerge", fm = "pa", oblique.scores = TRUE)
## Standardized loadings (pattern matrix) based upon correlation matrix
## PA1 PA2 PA3 PA5 PA6 PA4 PA7 h2 u2 com
## sentlen.m -0.68 -0.05 0.01 -0.21 0.04 0.38 -0.01 0.92 0.080 1.8
## sentcount 0.15 0.98 0.01 0.27 -0.10 -0.18 0.02 0.93 0.065 1.3
## activity 0.76 -0.03 0.10 0.46 0.01 0.29 0.09 0.90 0.100 2.1
## VERBfrac.m 0.89 -0.05 0.19 0.31 -0.03 0.08 0.06 0.90 0.096 1.4
## wordcount -0.13 0.95 0.00 0.01 0.01 -0.02 -0.07 0.89 0.112 1.1
## entropy 0.09 0.75 0.06 -0.08 0.04 -0.07 -0.45 0.87 0.135 1.7
## sentlen.v 0.07 0.00 0.77 0.26 0.01 -0.14 0.02 0.46 0.535 1.3
## predsubjdist.m -0.37 -0.01 0.27 0.05 -0.05 0.09 0.30 0.35 0.647 3.0
## compoundVERBs 1.03 -0.13 0.29 -0.36 0.01 -0.22 0.06 0.70 0.296 1.6
## passives -0.02 -0.09 -0.02 -0.76 0.11 -0.26 0.05 0.56 0.441 1.3
## predobjdist.m -0.04 -0.08 0.62 -0.04 -0.07 -0.07 0.15 0.39 0.613 1.2
## literary 0.00 -0.05 0.08 -0.30 0.15 0.14 -0.09 0.24 0.758 2.4
## verbdist -0.86 0.00 0.02 -0.12 -0.06 -0.22 0.10 0.80 0.197 1.2
## maentropy -0.22 0.02 -0.18 -0.11 0.04 -0.02 -0.64 0.50 0.499 1.5
## predorder.m -0.71 -0.05 0.09 0.02 -0.04 0.21 0.15 0.63 0.373 1.3
## hapaxes 0.12 -0.79 0.06 0.01 -0.03 -0.09 -0.22 0.68 0.318 1.2
## VERBcomp 0.57 0.02 -0.02 0.15 -0.13 0.52 -0.02 0.60 0.403 2.2
## NOUNcount.v -0.13 -0.08 0.46 0.00 0.00 0.02 -0.16 0.35 0.654 1.5
## subj 0.54 0.15 -0.17 -0.10 0.06 -0.03 0.30 0.56 0.436 2.1
## NOUNcount.m -0.90 0.04 0.02 -0.03 -0.13 -0.05 -0.07 0.81 0.193 1.1
## predobjdist.v 0.04 0.15 0.53 -0.06 0.07 0.05 0.00 0.40 0.604 1.3
## NEGcount.m -0.06 -0.08 -0.06 0.14 1.00 0.15 -0.01 0.95 0.054 1.1
## compoundVERBsdist.m 0.21 -0.03 0.75 -0.12 -0.07 -0.06 0.09 0.42 0.578 1.3
## VERBfrac.v -0.44 -0.04 0.17 0.25 -0.02 -0.19 -0.15 0.35 0.651 2.6
## NEGcount.v 0.21 0.07 0.02 0.02 0.74 0.06 -0.07 0.59 0.412 1.2
## compoundVERBsdist.v -0.09 0.23 0.30 -0.19 0.03 0.00 0.03 0.33 0.670 2.9
## predsubjdist.v -0.21 0.10 0.41 -0.02 0.10 0.14 0.03 0.46 0.536 2.1
## mamr 0.67 -0.03 -0.09 -0.04 -0.03 0.00 0.36 0.74 0.255 1.6
## obj 0.02 -0.06 -0.04 0.07 0.15 0.84 0.04 0.69 0.312 1.1
## predorder.v -0.09 -0.02 0.56 -0.05 0.06 0.17 -0.02 0.53 0.470 1.3
## NEGfrac.m -0.06 -0.03 -0.03 0.60 0.31 -0.17 0.17 0.41 0.592 1.9
##
## PA1 PA2 PA3 PA5 PA6 PA4 PA7
## SS loadings 6.71 3.09 2.77 1.83 1.74 1.53 1.24
## Proportion Var 0.22 0.10 0.09 0.06 0.06 0.05 0.04
## Cumulative Var 0.22 0.32 0.41 0.46 0.52 0.57 0.61
## Proportion Explained 0.35 0.16 0.15 0.10 0.09 0.08 0.07
## Cumulative Proportion 0.35 0.52 0.66 0.76 0.85 0.93 1.00
##
## With factor correlations of
## PA1 PA2 PA3 PA5 PA6 PA4 PA7
## PA1 1.00 0.12 -0.61 0.37 -0.27 -0.13 0.17
## PA2 0.12 1.00 0.15 -0.27 0.31 0.30 -0.08
## PA3 -0.61 0.15 1.00 -0.32 0.26 0.30 -0.12
## PA5 0.37 -0.27 -0.32 1.00 -0.38 -0.34 0.03
## PA6 -0.27 0.31 0.26 -0.38 1.00 0.22 -0.18
## PA4 -0.13 0.30 0.30 -0.34 0.22 1.00 -0.07
## PA7 0.17 -0.08 -0.12 0.03 -0.18 -0.07 1.00
##
## Mean item complexity = 1.6
## Test of the hypothesis that 7 factors are sufficient.
##
## df null model = 465 with the objective function = 22.85 with Chi Square = 16927.71
## df of the model are 269 and the objective function was 2.74
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.04
##
## The harmonic n.obs is 753 with the empirical chi square 517.31 with prob < 6.9e-18
## The total n.obs was 753 with Likelihood Chi Square = 2020.55 with prob < 1.4e-265
##
## Tucker Lewis Index of factoring reliability = 0.815
## RMSEA index = 0.093 and the 90 % confidence intervals are 0.089 0.097
## BIC = 238.68
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## PA1 PA2 PA3 PA5 PA6 PA4
## Correlation of (regression) scores with factors 0.98 0.98 0.93 0.93 0.98 0.93
## Multiple R square of scores with factors 0.97 0.96 0.86 0.87 0.96 0.87
## Minimum correlation of possible factor scores 0.94 0.92 0.73 0.74 0.91 0.75
## PA7
## Correlation of (regression) scores with factors 0.9
## Multiple R square of scores with factors 0.8
## Minimum correlation of possible factor scores 0.6
##
## Coefficients and bootstrapped confidence intervals
## low PA1 upper low PA2 upper low PA3 upper low
## sentlen.m -0.76 -0.68 -0.56 -0.09 -0.05 -0.02 -0.05 0.01 0.09 -0.26
## sentcount 0.11 0.15 0.21 0.93 0.98 1.03 -0.02 0.01 0.05 0.21
## activity 0.64 0.76 0.85 -0.06 -0.03 0.01 0.04 0.10 0.15 0.39
## VERBfrac.m 0.72 0.89 1.00 -0.08 -0.05 -0.01 0.10 0.19 0.24 0.25
## wordcount -0.16 -0.13 -0.07 0.92 0.95 0.98 -0.04 0.00 0.04 -0.03
## entropy -0.03 0.09 0.14 0.71 0.75 0.81 -0.04 0.06 0.11 -0.13
## sentlen.v -0.04 0.07 0.13 -0.06 0.00 0.07 0.62 0.77 0.89 0.19
## predsubjdist.m -0.53 -0.37 -0.23 -0.05 -0.01 0.06 0.11 0.27 0.41 -0.01
## compoundVERBs 0.79 1.03 1.15 -0.17 -0.13 -0.04 0.14 0.29 0.36 -0.44
## passives -0.10 -0.02 0.04 -0.13 -0.09 -0.04 -0.12 -0.02 0.05 -0.86
## predobjdist.m -0.26 -0.04 0.16 -0.17 -0.08 0.01 0.41 0.62 0.80 -0.16
## literary -0.10 0.00 0.09 -0.11 -0.05 0.02 -0.03 0.08 0.18 -0.39
## verbdist -0.94 -0.86 -0.74 -0.03 0.00 0.02 -0.02 0.02 0.08 -0.25
## maentropy -0.38 -0.22 -0.11 -0.03 0.02 0.11 -0.32 -0.18 -0.04 -0.20
## predorder.m -0.81 -0.71 -0.58 -0.09 -0.05 0.01 -0.02 0.09 0.20 -0.09
## hapaxes -0.01 0.12 0.17 -0.83 -0.79 -0.72 -0.06 0.06 0.12 -0.05
## VERBcomp 0.46 0.57 0.65 -0.04 0.02 0.08 -0.08 -0.02 0.05 0.08
## NOUNcount.v -0.27 -0.13 -0.01 -0.15 -0.08 -0.01 0.29 0.46 0.60 -0.09
## subj 0.45 0.54 0.62 0.09 0.15 0.20 -0.22 -0.17 -0.10 -0.18
## NOUNcount.m -1.01 -0.90 -0.74 -0.01 0.04 0.08 -0.04 0.02 0.10 -0.09
## predobjdist.v -0.09 0.04 0.17 0.05 0.15 0.25 0.40 0.53 0.66 -0.16
## NEGcount.m -0.11 -0.06 0.00 -0.11 -0.08 -0.03 -0.11 -0.06 0.00 0.05
## compoundVERBsdist.m 0.08 0.21 0.32 -0.09 -0.03 0.04 0.59 0.75 0.88 -0.20
## VERBfrac.v -0.57 -0.44 -0.32 -0.13 -0.04 0.04 0.04 0.17 0.25 0.14
## NEGcount.v 0.13 0.21 0.27 0.02 0.07 0.11 -0.04 0.02 0.07 -0.05
## compoundVERBsdist.v -0.24 -0.09 0.04 0.16 0.23 0.31 0.15 0.30 0.45 -0.31
## predsubjdist.v -0.33 -0.21 -0.08 0.02 0.10 0.17 0.27 0.41 0.54 -0.12
## mamr 0.57 0.67 0.78 -0.09 -0.03 0.01 -0.15 -0.09 -0.01 -0.11
## obj -0.03 0.02 0.10 -0.11 -0.06 -0.01 -0.10 -0.04 0.04 0.02
## predorder.v -0.23 -0.09 0.03 -0.07 -0.02 0.06 0.38 0.56 0.70 -0.14
## NEGfrac.m -0.13 -0.06 0.06 -0.10 -0.03 0.03 -0.12 -0.03 0.05 0.51
## PA5 upper low PA6 upper low PA4 upper low PA7
## sentlen.m -0.21 -0.17 0.02 0.04 0.09 0.33 0.38 0.45 -0.08 -0.01
## sentcount 0.27 0.32 -0.15 -0.10 -0.07 -0.23 -0.18 -0.14 -0.05 0.02
## activity 0.46 0.54 -0.03 0.01 0.06 0.25 0.29 0.37 0.05 0.09
## VERBfrac.m 0.31 0.40 -0.09 -0.03 0.03 0.01 0.08 0.17 -0.01 0.06
## wordcount 0.01 0.05 -0.03 0.01 0.05 -0.06 -0.02 0.01 -0.18 -0.07
## entropy -0.08 -0.01 -0.02 0.04 0.13 -0.14 -0.07 0.01 -0.57 -0.45
## sentlen.v 0.26 0.34 -0.07 0.01 0.07 -0.20 -0.14 -0.08 -0.06 0.02
## predsubjdist.m 0.05 0.16 -0.17 -0.05 0.08 -0.11 0.09 0.31 0.10 0.30
## compoundVERBs -0.36 -0.25 -0.07 0.01 0.08 -0.31 -0.22 -0.11 -0.03 0.06
## passives -0.76 -0.66 0.06 0.11 0.16 -0.33 -0.26 -0.21 -0.02 0.05
## predobjdist.m -0.04 0.09 -0.17 -0.07 0.04 -0.16 -0.07 0.02 -0.08 0.15
## literary -0.30 -0.21 0.08 0.15 0.25 0.05 0.14 0.23 -0.17 -0.09
## verbdist -0.12 -0.01 -0.10 -0.06 -0.01 -0.28 -0.22 -0.18 0.02 0.10
## maentropy -0.11 0.01 -0.05 0.04 0.17 -0.13 -0.02 0.10 -0.84 -0.64
## predorder.m 0.02 0.12 -0.16 -0.04 0.09 0.07 0.21 0.37 -0.04 0.15
## hapaxes 0.01 0.10 -0.10 -0.03 0.04 -0.16 -0.09 -0.01 -0.30 -0.22
## VERBcomp 0.15 0.24 -0.18 -0.13 -0.05 0.43 0.52 0.66 -0.11 -0.02
## NOUNcount.v 0.00 0.11 -0.08 0.00 0.08 -0.07 0.02 0.11 -0.35 -0.16
## subj -0.10 -0.03 0.00 0.06 0.14 -0.10 -0.03 0.02 0.19 0.30
## NOUNcount.m -0.03 0.03 -0.19 -0.13 -0.07 -0.14 -0.05 0.02 -0.18 -0.07
## predobjdist.v -0.06 0.04 -0.02 0.07 0.17 -0.04 0.05 0.18 -0.10 0.00
## NEGcount.m 0.14 0.19 0.82 1.00 1.16 0.11 0.15 0.24 -0.11 -0.01
## compoundVERBsdist.m -0.12 -0.05 -0.14 -0.07 0.01 -0.13 -0.06 0.01 0.00 0.09
## VERBfrac.v 0.25 0.38 -0.12 -0.02 0.10 -0.30 -0.19 -0.10 -0.32 -0.15
## NEGcount.v 0.02 0.11 0.64 0.74 0.93 0.00 0.06 0.15 -0.15 -0.07
## compoundVERBsdist.v -0.19 -0.08 -0.03 0.03 0.10 -0.09 0.00 0.09 -0.06 0.03
## predsubjdist.v -0.02 0.06 0.01 0.10 0.19 0.06 0.14 0.24 -0.12 0.03
## mamr -0.04 0.03 -0.11 -0.03 0.03 -0.06 0.00 0.07 0.24 0.36
## obj 0.07 0.13 0.09 0.15 0.24 0.77 0.84 0.97 -0.04 0.04
## predorder.v -0.05 0.04 -0.02 0.06 0.14 0.09 0.17 0.26 -0.14 -0.02
## NEGfrac.m 0.60 0.67 0.21 0.31 0.40 -0.25 -0.17 -0.08 0.06 0.17
## upper
## sentlen.m 0.03
## sentcount 0.07
## activity 0.17
## VERBfrac.m 0.18
## wordcount -0.03
## entropy -0.34
## sentlen.v 0.12
## predsubjdist.m 0.64
## compoundVERBs 0.22
## passives 0.10
## predobjdist.m 0.51
## literary -0.01
## verbdist 0.18
## maentropy -0.47
## predorder.m 0.35
## hapaxes -0.10
## VERBcomp 0.10
## NOUNcount.v -0.02
## subj 0.48
## NOUNcount.m -0.02
## predobjdist.v 0.12
## NEGcount.m 0.03
## compoundVERBsdist.m 0.19
## VERBfrac.v -0.02
## NEGcount.v 0.00
## compoundVERBsdist.v 0.12
## predsubjdist.v 0.18
## mamr 0.55
## obj 0.12
## predorder.v 0.08
## NEGfrac.m 0.28
##
## Interfactor correlations and bootstrapped confidence intervals
## lower estimate upper
## PA1-PA2 -0.489 0.118 0.50
## PA1-PA3 -1.165 -0.610 0.64
## PA1-PA5 -0.807 0.370 0.62
## PA1-PA6 -0.687 -0.268 0.53
## PA1-PA4 -0.444 -0.132 0.28
## PA1-PA7 -0.398 0.171 0.23
## PA2-PA3 -0.064 0.146 0.34
## PA2-PA5 -0.475 -0.266 0.59
## PA2-PA6 -0.321 0.313 0.64
## PA2-PA4 -0.127 0.297 0.56
## PA2-PA7 -0.297 -0.075 0.42
## PA3-PA5 -0.530 -0.324 0.68
## PA3-PA6 -0.299 0.259 0.64
## PA3-PA4 -0.081 0.301 0.54
## PA3-PA7 -0.244 -0.119 0.44
## PA5-PA6 -0.755 -0.377 0.67
## PA5-PA4 -0.501 -0.336 0.59
## PA5-PA7 -0.332 0.034 0.37
## PA6-PA4 -0.364 0.225 0.53
## PA6-PA7 -0.282 -0.184 0.35
## PA4-PA7 -0.342 -0.070 0.43Healthiness diagnostics
fa_broad$loadings[] %>%
as_tibble() %>%
mutate(feat = colnames(data_scaled)) %>%
select(feat, everything()) %>%
pivot_longer(!feat) %>%
mutate(value = abs(value)) %>%
group_by(feat) %>%
summarize(maxload = max(value)) %>%
arrange(maxload)## # A tibble: 31 × 2
## feat maxload
## <chr> <dbl>
## 1 literary 0.303
## 2 compoundVERBsdist.v 0.304
## 3 predsubjdist.m 0.370
## 4 predsubjdist.v 0.409
## 5 VERBfrac.v 0.444
## 6 NOUNcount.v 0.457
## 7 predobjdist.v 0.534
## 8 subj 0.536
## 9 predorder.v 0.555
## 10 VERBcomp 0.567
## # ℹ 21 more rowsfa_broad$communality %>% sort()## literary compoundVERBsdist.v NOUNcount.v VERBfrac.v
## 0.2416725 0.3296174 0.3462354 0.3490726
## predsubjdist.m predobjdist.m predobjdist.v NEGfrac.m
## 0.3534706 0.3870510 0.3956853 0.4080319
## compoundVERBsdist.m predsubjdist.v sentlen.v maentropy
## 0.4218770 0.4636951 0.4648207 0.5005586
## predorder.v passives subj NEGcount.v
## 0.5304832 0.5593128 0.5636361 0.5882451
## VERBcomp predorder.m hapaxes obj
## 0.5973404 0.6266506 0.6822421 0.6883013
## compoundVERBs mamr verbdist NOUNcount.m
## 0.7035859 0.7447957 0.8034091 0.8071553
## entropy wordcount activity VERBfrac.m
## 0.8654729 0.8878834 0.9002538 0.9036643
## sentlen.m sentcount NEGcount.m
## 0.9199822 0.9349906 0.9460857fa_broad$communality[fa_broad$communality < 0.5] %>% names()## [1] "sentlen.v" "predsubjdist.m" "predobjdist.m"
## [4] "literary" "NOUNcount.v" "predobjdist.v"
## [7] "compoundVERBsdist.m" "VERBfrac.v" "compoundVERBsdist.v"
## [10] "predsubjdist.v" "NEGfrac.m"fa_broad$complexity %>% sort()## wordcount NOUNcount.m obj NEGcount.m
## 1.050148 1.068934 1.095678 1.111812
## NEGcount.v verbdist predobjdist.m hapaxes
## 1.216647 1.220211 1.232746 1.243273
## predobjdist.v compoundVERBsdist.m predorder.v sentcount
## 1.257433 1.275774 1.290328 1.294705
## sentlen.v predorder.m passives VERBfrac.m
## 1.318462 1.321357 1.324733 1.379623
## NOUNcount.v maentropy compoundVERBs mamr
## 1.476835 1.486361 1.571818 1.596350
## entropy sentlen.m NEGfrac.m activity
## 1.736212 1.818851 1.916877 2.072686
## predsubjdist.v subj VERBcomp literary
## 2.087153 2.116688 2.247216 2.422278
## VERBfrac.v compoundVERBsdist.v predsubjdist.m
## 2.639408 2.919631 3.017975fa_broad$complexity[fa_broad$complexity > 2] %>% names()## [1] "activity" "predsubjdist.m" "literary"
## [4] "VERBcomp" "subj" "VERBfrac.v"
## [7] "compoundVERBsdist.v" "predsubjdist.v"Loadings
Comrey and Lee (1992): loadings excelent > .70 > very good > .63 > good > .55 > fair > .45 > poor > .32
fa.diagram(fa_broad)
fa_broad$loadings##
## Loadings:
## PA1 PA2 PA3 PA5 PA6 PA4 PA7
## sentlen.m -0.679 -0.215 0.376
## sentcount 0.153 0.982 0.267 -0.179
## activity 0.758 0.103 0.460 0.290
## VERBfrac.m 0.892 0.191 0.311
## wordcount -0.126 0.946
## entropy 0.748 -0.448
## sentlen.v 0.773 0.261 -0.139
## predsubjdist.m -0.370 0.265 0.298
## compoundVERBs 1.026 -0.126 0.287 -0.363 -0.221
## passives -0.761 0.112 -0.262
## predobjdist.m 0.615 0.153
## literary -0.303 0.152 0.138
## verbdist -0.863 -0.125 -0.224 0.102
## maentropy -0.219 -0.176 -0.115 -0.638
## predorder.m -0.706 0.207 0.152
## hapaxes 0.119 -0.789 -0.217
## VERBcomp 0.567 0.147 -0.129 0.520
## NOUNcount.v -0.129 0.457 -0.156
## subj 0.536 0.148 -0.170 0.297
## NOUNcount.m -0.902 -0.129
## predobjdist.v 0.151 0.534
## NEGcount.m 0.141 0.997 0.149
## compoundVERBsdist.m 0.214 0.754 -0.118
## VERBfrac.v -0.444 0.165 0.249 -0.188 -0.145
## NEGcount.v 0.210 0.740
## compoundVERBsdist.v 0.234 0.304 -0.191
## predsubjdist.v -0.208 0.101 0.409 0.100 0.140
## mamr 0.671 0.361
## obj 0.146 0.844
## predorder.v 0.555 0.166
## NEGfrac.m 0.599 0.309 -0.167 0.172
##
## PA1 PA2 PA3 PA5 PA6 PA4 PA7
## SS loadings 6.625 3.220 2.930 1.874 1.786 1.668 1.150
## Proportion Var 0.214 0.104 0.095 0.060 0.058 0.054 0.037
## Cumulative Var 0.214 0.318 0.412 0.473 0.530 0.584 0.621for (i in 1:fa_broad$factors) {
cat("\n-----", colnames(fa_broad$loadings)[i], "-----\n")
loadings <- fa_broad$loadings[, i]
load_df <- data.frame(loading = loadings)
load_df_filtered <- load_df %>%
mutate(abs_l = abs(loading)) %>%
mutate(strng = case_when(
abs_l > 0.70 ~ "*****",
abs_l <= 0.70 & abs_l > 0.63 ~ "**** ",
abs_l <= 0.63 & abs_l > 0.55 ~ "*** ",
abs_l <= 0.55 & abs_l > 0.45 ~ "** ",
abs_l <= 0.45 & abs_l > 0.32 ~ "* ",
.default = ""
)) %>%
arrange(-abs_l) %>%
filter(abs_l > 0.1)
load_df_filtered %>%
mutate(across(c(loading, abs_l), ~ round(.x, 3))) %>%
print()
cat("\n")
}##
## ----- PA1 -----
## loading abs_l strng
## compoundVERBs 1.026 1.026 *****
## NOUNcount.m -0.902 0.902 *****
## VERBfrac.m 0.892 0.892 *****
## verbdist -0.863 0.863 *****
## activity 0.758 0.758 *****
## predorder.m -0.706 0.706 *****
## sentlen.m -0.679 0.679 ****
## mamr 0.671 0.671 ****
## VERBcomp 0.567 0.567 ***
## subj 0.536 0.536 **
## VERBfrac.v -0.444 0.444 *
## predsubjdist.m -0.370 0.370 *
## maentropy -0.219 0.219
## compoundVERBsdist.m 0.214 0.214
## NEGcount.v 0.210 0.210
## predsubjdist.v -0.208 0.208
## sentcount 0.153 0.153
## NOUNcount.v -0.129 0.129
## wordcount -0.126 0.126
## hapaxes 0.119 0.119
##
##
## ----- PA2 -----
## loading abs_l strng
## sentcount 0.982 0.982 *****
## wordcount 0.946 0.946 *****
## hapaxes -0.789 0.789 *****
## entropy 0.748 0.748 *****
## compoundVERBsdist.v 0.234 0.234
## predobjdist.v 0.151 0.151
## subj 0.148 0.148
## compoundVERBs -0.126 0.126
## predsubjdist.v 0.101 0.101
##
##
## ----- PA3 -----
## loading abs_l strng
## sentlen.v 0.773 0.773 *****
## compoundVERBsdist.m 0.754 0.754 *****
## predobjdist.m 0.615 0.615 ***
## predorder.v 0.555 0.555 ***
## predobjdist.v 0.534 0.534 **
## NOUNcount.v 0.457 0.457 **
## predsubjdist.v 0.409 0.409 *
## compoundVERBsdist.v 0.304 0.304
## compoundVERBs 0.287 0.287
## predsubjdist.m 0.265 0.265
## VERBfrac.m 0.191 0.191
## maentropy -0.176 0.176
## subj -0.170 0.170
## VERBfrac.v 0.165 0.165
## activity 0.103 0.103
##
##
## ----- PA5 -----
## loading abs_l strng
## passives -0.761 0.761 *****
## NEGfrac.m 0.599 0.599 ***
## activity 0.460 0.460 **
## compoundVERBs -0.363 0.363 *
## VERBfrac.m 0.311 0.311
## literary -0.303 0.303
## sentcount 0.267 0.267
## sentlen.v 0.261 0.261
## VERBfrac.v 0.249 0.249
## sentlen.m -0.215 0.215
## compoundVERBsdist.v -0.191 0.191
## VERBcomp 0.147 0.147
## NEGcount.m 0.141 0.141
## verbdist -0.125 0.125
## compoundVERBsdist.m -0.118 0.118
## maentropy -0.115 0.115
##
##
## ----- PA6 -----
## loading abs_l strng
## NEGcount.m 0.997 0.997 *****
## NEGcount.v 0.740 0.740 *****
## NEGfrac.m 0.309 0.309
## literary 0.152 0.152
## obj 0.146 0.146
## VERBcomp -0.129 0.129
## NOUNcount.m -0.129 0.129
## passives 0.112 0.112
## predsubjdist.v 0.100 0.100
##
##
## ----- PA4 -----
## loading abs_l strng
## obj 0.844 0.844 *****
## VERBcomp 0.520 0.520 **
## sentlen.m 0.376 0.376 *
## activity 0.290 0.290
## passives -0.262 0.262
## verbdist -0.224 0.224
## compoundVERBs -0.221 0.221
## predorder.m 0.207 0.207
## VERBfrac.v -0.188 0.188
## sentcount -0.179 0.179
## NEGfrac.m -0.167 0.167
## predorder.v 0.166 0.166
## NEGcount.m 0.149 0.149
## predsubjdist.v 0.140 0.140
## sentlen.v -0.139 0.139
## literary 0.138 0.138
##
##
## ----- PA7 -----
## loading abs_l strng
## maentropy -0.638 0.638 ****
## entropy -0.448 0.448 *
## mamr 0.361 0.361 *
## predsubjdist.m 0.298 0.298
## subj 0.297 0.297
## hapaxes -0.217 0.217
## NEGfrac.m 0.172 0.172
## NOUNcount.v -0.156 0.156
## predobjdist.m 0.153 0.153
## predorder.m 0.152 0.152
## VERBfrac.v -0.145 0.145
## verbdist 0.102 0.102hypotheses:
Uniquenesses
fa_broad$uniquenesses %>% round(3)## sentlen.m sentcount activity VERBfrac.m
## 0.080 0.065 0.100 0.096
## wordcount entropy sentlen.v predsubjdist.m
## 0.112 0.135 0.535 0.647
## compoundVERBs passives predobjdist.m literary
## 0.296 0.441 0.613 0.758
## verbdist maentropy predorder.m hapaxes
## 0.197 0.499 0.373 0.318
## VERBcomp NOUNcount.v subj NOUNcount.m
## 0.403 0.654 0.436 0.193
## predobjdist.v NEGcount.m compoundVERBsdist.m VERBfrac.v
## 0.604 0.054 0.578 0.651
## NEGcount.v compoundVERBsdist.v predsubjdist.v mamr
## 0.412 0.670 0.536 0.255
## obj predorder.v NEGfrac.m
## 0.312 0.470 0.592Distributions over factors
broad_data <- data_factor_bind(data_clean, fa_broad)
broad_data$data %>% write_csv("data_w_factors.csv")
broad_data$long %>%
group_by(factor) %>%
summarize(shapiro = shapiro.test(factor_score)$p.value)## # A tibble: 7 × 2
## factor shapiro
## <fct> <dbl>
## 1 PA1 2.98e-13
## 2 PA2 2.39e-14
## 3 PA3 7.87e-33
## 4 PA5 1.32e- 3
## 5 PA6 6.04e-12
## 6 PA4 1.43e-14
## 7 PA7 1.69e-11broad_data$long %>%
ggplot(aes(x = factor_score, y = class)) +
facet_grid(factor ~ .) +
theme(legend.position = "bottom") +
geom_jitter(width = 0, height = 0.1, alpha = 0.2)
class
analyze_distributions(broad_data$long, "class")##
## bad good
## 414 339## Saving 7 x 5 in image
## Saving 7 x 5 in image
##
## Test for the significance of differences in class over PA1 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 123.4655, df = 1, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | bad
## ---------+-----------
## good | -11.11150
## | 0.0000*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.164 (95% CI: 0.115 - 0.218 )
##
## Test for the significance of differences in class over PA2 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 0.866, df = 1, p-value = 0.35
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | bad
## ---------+-----------
## good | 0.930602
## | 0.3521
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.00115 (95% CI: 3.55e-06 - 0.011 )
##
## Test for the significance of differences in class over PA3 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 12.2358, df = 1, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | bad
## ---------+-----------
## good | 3.497969
## | 0.0005*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.0163 (95% CI: 0.00315 - 0.0391 )
##
## Test for the significance of differences in class over PA5 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 97.8011, df = 1, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | bad
## ---------+-----------
## good | -9.889444
## | 0.0000*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.13 (95% CI: 0.0861 - 0.179 )
##
## Test for the significance of differences in class over PA6 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 32.3171, df = 1, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | bad
## ---------+-----------
## good | 5.684810
## | 0.0000*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.043 (95% CI: 0.0189 - 0.0776 )
##
## Test for the significance of differences in class over PA4 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 2.5333, df = 1, p-value = 0.11
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | bad
## ---------+-----------
## good | 1.591639
## | 0.1115
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.00337 (95% CI: 3.02e-05 - 0.0165 )
##
## Test for the significance of differences in class over PA7 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 64.2257, df = 1, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | bad
## ---------+-----------
## good | -8.014095
## | 0.0000*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.0854 (95% CI: 0.0507 - 0.126 )
##
## factor chi2 kruskal_p epsilon2_lci epsilon2 epsilon2_uci
## 1 PA1 123.47 < 0.0001 0.115 0.164 0.218
## 2 PA2 0.87 0.35 0.000 0.001 0.011
## 3 PA3 12.24 < 0.001 0.003 0.016 0.039
## 4 PA5 97.80 < 0.0001 0.086 0.130 0.179
## 5 PA6 32.32 < 0.0001 0.019 0.043 0.078
## 6 PA4 2.53 0.11 0.000 0.003 0.016
## 7 PA7 64.23 < 0.0001 0.051 0.085 0.126
##
## p < 5e-2 found in: PA1 PA3 PA5 PA6 PA7
## p < 1e-2 found in: PA1 PA3 PA5 PA6 PA7
## p < 1e-3 found in: PA1 PA3 PA5 PA6 PA7
## p < 1e-4 found in: PA1 PA5 PA6 PA7subcorpus
analyze_distributions(broad_data$long, "subcorpus")##
## CzCDC FrBo KUKY LiFRLaw OmbuFlyers
## 211 307 194 3 38## Saving 7 x 5 in image
## Saving 7 x 5 in image
##
## Test for the significance of differences in subcorpus over PA1 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 377.3425, df = 4, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | CzCDC FrBo KUKY LiFRLaw
## ---------+--------------------------------------------
## FrBo | -18.60055
## | 0.0000*
## |
## KUKY | -5.567728 12.09728
## | 0.0000* 0.0000*
## |
## LiFRLaw | -1.250078 1.614183 -0.297410
## | 1.0000 1.0000 1.0000
## |
## OmbuFlye | -7.027404 2.471010 -3.859030 -0.853008
## | 0.0000* 0.1347 0.0011* 1.0000
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.502 (95% CI: 0.462 - 0.551 )
##
## Test for the significance of differences in subcorpus over PA2 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 6.7889, df = 4, p-value = 0.15
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | CzCDC FrBo KUKY LiFRLaw
## ---------+--------------------------------------------
## FrBo | -0.547372
## | 1.0000
## |
## KUKY | 1.439325 2.094663
## | 1.0000 0.3620
## |
## LiFRLaw | 1.234977 1.322062 0.988141
## | 1.0000 1.0000 1.0000
## |
## OmbuFlye | -0.747903 -0.481779 -1.549989 -1.417131
## | 1.0000 1.0000 1.0000 1.0000
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.00903 (95% CI: 0.00286 - 0.0315 )
##
## Test for the significance of differences in subcorpus over PA3 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 45.1488, df = 4, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | CzCDC FrBo KUKY LiFRLaw
## ---------+--------------------------------------------
## FrBo | 4.943535
## | 0.0000*
## |
## KUKY | -0.564893 -5.432595
## | 1.0000 0.0000*
## |
## LiFRLaw | 2.166509 1.409297 2.261758
## | 0.3027 1.0000 0.2371
## |
## OmbuFlye | -0.590289 -3.175552 -0.269642 -2.273973
## | 1.0000 0.0150* 1.0000 0.2297
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.06 (95% CI: 0.0359 - 0.0987 )
##
## Test for the significance of differences in subcorpus over PA5 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 143.9294, df = 4, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | CzCDC FrBo KUKY LiFRLaw
## ---------+--------------------------------------------
## FrBo | -10.26999
## | 0.0000*
## |
## KUKY | -9.824054 -0.641089
## | 0.0000* 1.0000
## |
## LiFRLaw | 0.858009 2.442870 2.537078
## | 1.0000 0.1457 0.1118
## |
## OmbuFlye | -6.398949 -1.216896 -0.848195 -2.712212
## | 0.0000* 1.0000 1.0000 0.0668
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.191 (95% CI: 0.147 - 0.246 )
##
## Test for the significance of differences in subcorpus over PA6 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 82.3697, df = 4, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | CzCDC FrBo KUKY LiFRLaw
## ---------+--------------------------------------------
## FrBo | 8.966160
## | 0.0000*
## |
## KUKY | 3.984597 -4.420660
## | 0.0007* 0.0001*
## |
## LiFRLaw | 1.823567 0.445571 1.141211
## | 0.6822 1.0000 1.0000
## |
## OmbuFlye | 2.617962 -1.979672 0.366449 -0.998729
## | 0.0885 0.4774 1.0000 1.0000
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.11 (95% CI: 0.0764 - 0.16 )
##
## Test for the significance of differences in subcorpus over PA4 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 51.7167, df = 4, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | CzCDC FrBo KUKY LiFRLaw
## ---------+--------------------------------------------
## FrBo | 6.203533
## | 0.0000*
## |
## KUKY | 4.012379 -1.696963
## | 0.0006* 0.8970
## |
## LiFRLaw | 0.700048 -0.254598 0.013631
## | 1.0000 1.0000 1.0000
## |
## OmbuFlye | 5.319030 2.224822 3.034058 0.884278
## | 0.0000* 0.2609 0.0241* 1.0000
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.0688 (95% CI: 0.0418 - 0.113 )
##
## Test for the significance of differences in subcorpus over PA7 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 43.9067, df = 4, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | CzCDC FrBo KUKY LiFRLaw
## ---------+--------------------------------------------
## FrBo | -5.358556
## | 0.0000*
## |
## KUKY | -2.336286 2.690861
## | 0.1948 0.0713
## |
## LiFRLaw | 0.629436 1.456765 1.028479
## | 1.0000 1.0000 1.0000
## |
## OmbuFlye | 1.968306 4.803489 3.265242 -0.031874
## | 0.4903 0.0000* 0.0109* 1.0000
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.0584 (95% CI: 0.0352 - 0.0991 )
##
## factor chi2 kruskal_p epsilon2_lci epsilon2 epsilon2_uci
## 1 PA1 377.34 < 0.0001 0.462 0.502 0.551
## 2 PA2 6.79 0.15 0.003 0.009 0.032
## 3 PA3 45.15 < 0.0001 0.036 0.060 0.099
## 4 PA5 143.93 < 0.0001 0.147 0.191 0.246
## 5 PA6 82.37 < 0.0001 0.076 0.110 0.160
## 6 PA4 51.72 < 0.0001 0.042 0.069 0.113
## 7 PA7 43.91 < 0.0001 0.035 0.058 0.099
##
## p < 5e-2 found in: PA1 PA3 PA5 PA6 PA4 PA7
## p < 1e-2 found in: PA1 PA3 PA5 PA6 PA4 PA7
## p < 1e-3 found in: PA1 PA3 PA5 PA6 PA4 PA7
## p < 1e-4 found in: PA1 PA3 PA5 PA6 PA4 PA7subcorpus wo/ LiFRLaw
analyze_distributions(
broad_data$long %>% filter(subcorpus != "LiFRLaw"), "subcorpus"
)##
## CzCDC FrBo KUKY LiFRLaw OmbuFlyers
## 211 307 194 0 38## Saving 7 x 5 in image
## Saving 7 x 5 in image
##
## Test for the significance of differences in subcorpus over PA1 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 376.5495, df = 3, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | CzCDC FrBo KUKY
## ---------+---------------------------------
## FrBo | -18.58246
## | 0.0000*
## |
## KUKY | -5.560238 12.08776
## | 0.0000* 0.0000*
## |
## OmbuFlye | -7.018545 2.470679 -3.854430
## | 0.0000* 0.0809 0.0007*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.503 (95% CI: 0.461 - 0.549 )
##
## Test for the significance of differences in subcorpus over PA2 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 5.238, df = 3, p-value = 0.16
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | CzCDC FrBo KUKY
## ---------+---------------------------------
## FrBo | -0.543387
## | 1.0000
## |
## KUKY | 1.431965 2.082795
## | 0.9129 0.2236
## |
## OmbuFlye | -0.745973 -0.481873 -1.543944
## | 1.0000 1.0000 0.7356
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.00699 (95% CI: 0.00108 - 0.0268 )
##
## Test for the significance of differences in subcorpus over PA3 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 41.7665, df = 3, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | CzCDC FrBo KUKY
## ---------+---------------------------------
## FrBo | 4.955310
## | 0.0000*
## |
## KUKY | -0.570035 -5.449652
## | 1.0000 0.0000*
## |
## OmbuFlye | -0.589885 -3.181261 -0.266358
## | 1.0000 0.0088* 1.0000
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.0558 (95% CI: 0.0306 - 0.0912 )
##
## Test for the significance of differences in subcorpus over PA5 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 139.9113, df = 3, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | CzCDC FrBo KUKY
## ---------+---------------------------------
## FrBo | -10.27201
## | 0.0000*
## |
## KUKY | -9.835261 -0.651277
## | 0.0000* 1.0000
## |
## OmbuFlye | -6.402968 -1.219965 -0.845903
## | 0.0000* 1.0000 1.0000
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.187 (95% CI: 0.136 - 0.244 )
##
## Test for the significance of differences in subcorpus over PA6 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 81.3197, df = 3, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | CzCDC FrBo KUKY
## ---------+---------------------------------
## FrBo | 8.968444
## | 0.0000*
## |
## KUKY | 3.982588 -4.425067
## | 0.0004* 0.0001*
## |
## OmbuFlye | 2.617627 -1.981203 0.367243
## | 0.0531 0.2854 1.0000
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.109 (95% CI: 0.0737 - 0.16 )
##
## Test for the significance of differences in subcorpus over PA4 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 51.6735, df = 3, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | CzCDC FrBo KUKY
## ---------+---------------------------------
## FrBo | 6.201584
## | 0.0000*
## |
## KUKY | 4.010016 -1.697626
## | 0.0004* 0.5375
## |
## OmbuFlye | 5.316390 2.223129 3.032760
## | 0.0000* 0.1572 0.0145*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.069 (95% CI: 0.0404 - 0.113 )
##
## Test for the significance of differences in subcorpus over PA7 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 42.7952, df = 3, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | CzCDC FrBo KUKY
## ---------+---------------------------------
## FrBo | -5.358239
## | 0.0000*
## |
## KUKY | -2.336550 2.690265
## | 0.1168 0.0428*
## |
## OmbuFlye | 1.966417 4.801387 3.263513
## | 0.2955 0.0000* 0.0066*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.0571 (95% CI: 0.032 - 0.0962 )
##
## factor chi2 kruskal_p epsilon2_lci epsilon2 epsilon2_uci
## 1 PA1 376.55 < 0.0001 0.461 0.503 0.549
## 2 PA2 5.24 0.16 0.001 0.007 0.027
## 3 PA3 41.77 < 0.0001 0.031 0.056 0.091
## 4 PA5 139.91 < 0.0001 0.136 0.187 0.244
## 5 PA6 81.32 < 0.0001 0.074 0.109 0.160
## 6 PA4 51.67 < 0.0001 0.040 0.069 0.113
## 7 PA7 42.80 < 0.0001 0.032 0.057 0.096
##
## p < 5e-2 found in: PA1 PA3 PA5 PA6 PA4 PA7
## p < 1e-2 found in: PA1 PA3 PA5 PA6 PA4 PA7
## p < 1e-3 found in: PA1 PA3 PA5 PA6 PA4 PA7
## p < 1e-4 found in: PA1 PA3 PA5 PA6 PA4 PA7AuthorType
analyze_distributions(broad_data$long, "AuthorType")##
## authority individual <NA>
## 411 339 3## Saving 7 x 5 in image
## Saving 7 x 5 in image
##
## Test for the significance of differences in AuthorType over PA1 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 322.2485, df = 1, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | authorit
## ---------+-----------
## individu | -17.95127
## | 0.0000*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.429 (95% CI: 0.374 - 0.488 )
##
## Test for the significance of differences in AuthorType over PA2 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 0.0091, df = 1, p-value = 0.92
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | authorit
## ---------+-----------
## individu | 0.095329
## | 0.9241
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 1.21e-05 (95% CI: 1.31e-06 - 0.00731 )
##
## Test for the significance of differences in AuthorType over PA3 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 45.0354, df = 1, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | authorit
## ---------+-----------
## individu | 6.710839
## | 0.0000*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.0599 (95% CI: 0.0318 - 0.097 )
##
## Test for the significance of differences in AuthorType over PA5 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 17.197, df = 1, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | authorit
## ---------+-----------
## individu | -4.146927
## | 0.0000*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.0229 (95% CI: 0.00703 - 0.0486 )
##
## Test for the significance of differences in AuthorType over PA6 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 54.5596, df = 1, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | authorit
## ---------+-----------
## individu | 7.386444
## | 0.0000*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.0726 (95% CI: 0.0409 - 0.113 )
##
## Test for the significance of differences in AuthorType over PA4 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 17.8605, df = 1, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | authorit
## ---------+-----------
## individu | 4.226171
## | 0.0000*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.0238 (95% CI: 0.00677 - 0.0485 )
##
## Test for the significance of differences in AuthorType over PA7 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 31.3267, df = 1, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | authorit
## ---------+-----------
## individu | -5.597022
## | 0.0000*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.0417 (95% CI: 0.0184 - 0.0737 )
##
## factor chi2 kruskal_p epsilon2_lci epsilon2 epsilon2_uci
## 1 PA1 322.25 < 0.0001 0.374 0.429 0.488
## 2 PA2 0.01 0.92 0.000 0.000 0.007
## 3 PA3 45.04 < 0.0001 0.032 0.060 0.097
## 4 PA5 17.20 < 0.0001 0.007 0.023 0.049
## 5 PA6 54.56 < 0.0001 0.041 0.073 0.113
## 6 PA4 17.86 < 0.0001 0.007 0.024 0.048
## 7 PA7 31.33 < 0.0001 0.018 0.042 0.074
##
## p < 5e-2 found in: PA1 PA3 PA5 PA6 PA4 PA7
## p < 1e-2 found in: PA1 PA3 PA5 PA6 PA4 PA7
## p < 1e-3 found in: PA1 PA3 PA5 PA6 PA4 PA7
## p < 1e-4 found in: PA1 PA3 PA5 PA6 PA4 PA7RecipientType
analyze_distributions(broad_data$long, "RecipientType")##
## combined legal person natural person <NA>
## 304 23 413 13## Saving 7 x 5 in image
## Saving 7 x 5 in image
##
## Test for the significance of differences in RecipientType over PA1 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 314.5305, df = 2, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | combined legal pe
## ---------+----------------------
## legal pe | -2.565495
## | 0.0309*
## |
## natural | -17.70569 -3.655701
## | 0.0000* 0.0008*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.418 (95% CI: 0.359 - 0.478 )
##
## Test for the significance of differences in RecipientType over PA2 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 16.3093, df = 2, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | combined legal pe
## ---------+----------------------
## legal pe | 3.658195
## | 0.0008*
## |
## natural | 2.412131 -2.841796
## | 0.0476* 0.0135*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.0217 (95% CI: 0.00646 - 0.0505 )
##
## Test for the significance of differences in RecipientType over PA3 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 20.0099, df = 2, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | combined legal pe
## ---------+----------------------
## legal pe | 1.654730
## | 0.2939
## |
## natural | 4.403938 -0.116900
## | 0.0000* 1.0000
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.0266 (95% CI: 0.00874 - 0.0583 )
##
## Test for the significance of differences in RecipientType over PA5 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 74.4874, df = 2, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | combined legal pe
## ---------+----------------------
## legal pe | -0.546314
## | 1.0000
## |
## natural | -8.546976 -2.463325
## | 0.0000* 0.0413*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.0991 (95% CI: 0.0622 - 0.147 )
##
## Test for the significance of differences in RecipientType over PA6 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 92.3301, df = 2, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | combined legal pe
## ---------+----------------------
## legal pe | 1.077348
## | 0.8440
## |
## natural | 9.569695 2.288037
## | 0.0000* 0.0664
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.123 (95% CI: 0.0825 - 0.174 )
##
## Test for the significance of differences in RecipientType over PA4 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 35.0423, df = 2, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | combined legal pe
## ---------+----------------------
## legal pe | 2.301815
## | 0.0640
## |
## natural | 5.805495 -0.275704
## | 0.0000* 1.0000
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.0466 (95% CI: 0.0214 - 0.0831 )
##
## Test for the significance of differences in RecipientType over PA7 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 19.2854, df = 2, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | combined legal pe
## ---------+----------------------
## legal pe | -1.109359
## | 0.8018
## |
## natural | -4.385409 -0.427067
## | 0.0000* 1.0000
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.0256 (95% CI: 0.00909 - 0.0532 )
##
## factor chi2 kruskal_p epsilon2_lci epsilon2 epsilon2_uci
## 1 PA1 314.53 < 0.0001 0.359 0.418 0.478
## 2 PA2 16.31 < 0.001 0.006 0.022 0.051
## 3 PA3 20.01 < 0.0001 0.009 0.027 0.058
## 4 PA5 74.49 < 0.0001 0.062 0.099 0.147
## 5 PA6 92.33 < 0.0001 0.082 0.123 0.174
## 6 PA4 35.04 < 0.0001 0.021 0.047 0.083
## 7 PA7 19.29 < 0.0001 0.009 0.026 0.053
##
## p < 5e-2 found in: PA1 PA2 PA3 PA5 PA6 PA4 PA7
## p < 1e-2 found in: PA1 PA2 PA3 PA5 PA6 PA4 PA7
## p < 1e-3 found in: PA1 PA2 PA3 PA5 PA6 PA4 PA7
## p < 1e-4 found in: PA1 PA3 PA5 PA6 PA4 PA7court decisions often with RecipientType = combined.
RecipientIndividuation
analyze_distributions(broad_data$long, "RecipientIndividuation")##
## bulk individual public <NA>
## 69 356 319 9## Saving 7 x 5 in image
## Saving 7 x 5 in image
##
## Test for the significance of differences in RecipientIndividuation over PA1 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 233.132, df = 2, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | bulk individu
## ---------+----------------------
## individu | -1.103793
## | 0.8090
## |
## public | -9.412970 -14.32708
## | 0.0000* 0.0000*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.31 (95% CI: 0.257 - 0.363 )
##
## Test for the significance of differences in RecipientIndividuation over PA2 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 41.5502, df = 2, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | bulk individu
## ---------+----------------------
## individu | 5.864716
## | 0.0000*
## |
## public | 3.374456 -4.194765
## | 0.0022* 0.0001*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.0553 (95% CI: 0.0276 - 0.092 )
##
## Test for the significance of differences in RecipientIndividuation over PA3 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 13.9732, df = 2, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | bulk individu
## ---------+----------------------
## individu | 0.492146
## | 1.0000
## |
## public | 2.475948 3.424222
## | 0.0399* 0.0018*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.0186 (95% CI: 0.0049 - 0.0471 )
##
## Test for the significance of differences in RecipientIndividuation over PA5 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 108.2741, df = 2, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | bulk individu
## ---------+----------------------
## individu | 5.699792
## | 0.0000*
## |
## public | -0.127232 -9.943723
## | 1.0000 0.0000*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.144 (95% CI: 0.0989 - 0.199 )
##
## Test for the significance of differences in RecipientIndividuation over PA6 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 42.0919, df = 2, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | bulk individu
## ---------+----------------------
## individu | 1.618330
## | 0.3168
## |
## public | 4.848507 5.588641
## | 0.0000* 0.0000*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.056 (95% CI: 0.0323 - 0.0963 )
##
## Test for the significance of differences in RecipientIndividuation over PA4 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 4.0916, df = 2, p-value = 0.13
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | bulk individu
## ---------+----------------------
## individu | -0.714174
## | 1.0000
## |
## public | 0.463258 2.016266
## | 1.0000 0.1313
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.00544 (95% CI: 0.000386 - 0.0235 )
##
## Test for the significance of differences in RecipientIndividuation over PA7 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 42.8594, df = 2, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | bulk individu
## ---------+----------------------
## individu | -0.544438
## | 1.0000
## |
## public | -4.091940 -6.117944
## | 0.0001* 0.0000*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.057 (95% CI: 0.0306 - 0.099 )
##
## factor chi2 kruskal_p epsilon2_lci epsilon2 epsilon2_uci
## 1 PA1 233.13 < 0.0001 0.257 0.310 0.363
## 2 PA2 41.55 < 0.0001 0.028 0.055 0.092
## 3 PA3 13.97 < 0.001 0.005 0.019 0.047
## 4 PA5 108.27 < 0.0001 0.099 0.144 0.199
## 5 PA6 42.09 < 0.0001 0.032 0.056 0.096
## 6 PA4 4.09 0.13 0.000 0.005 0.024
## 7 PA7 42.86 < 0.0001 0.031 0.057 0.099
##
## p < 5e-2 found in: PA1 PA2 PA3 PA5 PA6 PA7
## p < 1e-2 found in: PA1 PA2 PA3 PA5 PA6 PA7
## p < 1e-3 found in: PA1 PA2 PA5 PA6 PA7
## p < 1e-4 found in: PA1 PA2 PA5 PA6 PA7Objectivity
analyze_distributions(broad_data$long, "Objectivity")##
## persuasive quasiobjective <NA>
## 21 729 3## Saving 7 x 5 in image
## Saving 7 x 5 in image
##
## Test for the significance of differences in Objectivity over PA1 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 0.3232, df = 1, p-value = 0.57
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | persuasi
## ---------+-----------
## quasiobj | -0.568541
## | 0.5697
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.00043 (95% CI: 1.1e-06 - 0.00582 )
##
## Test for the significance of differences in Objectivity over PA2 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 5.9196, df = 1, p-value = 0.01
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | persuasi
## ---------+-----------
## quasiobj | -2.433032
## | 0.0150*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.00787 (95% CI: 0.000313 - 0.0233 )
##
## Test for the significance of differences in Objectivity over PA3 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 0.9549, df = 1, p-value = 0.33
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | persuasi
## ---------+-----------
## quasiobj | -0.977197
## | 0.3285
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.00127 (95% CI: 2.82e-06 - 0.0121 )
##
## Test for the significance of differences in Objectivity over PA5 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 2.8261, df = 1, p-value = 0.09
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | persuasi
## ---------+-----------
## quasiobj | -1.681106
## | 0.0927
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.00376 (95% CI: 1.17e-05 - 0.0251 )
##
## Test for the significance of differences in Objectivity over PA6 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 0.7532, df = 1, p-value = 0.39
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | persuasi
## ---------+-----------
## quasiobj | 0.867881
## | 0.3855
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.001 (95% CI: 6.13e-06 - 0.0181 )
##
## Test for the significance of differences in Objectivity over PA4 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 1.0469, df = 1, p-value = 0.31
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | persuasi
## ---------+-----------
## quasiobj | -1.023170
## | 0.3062
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.00139 (95% CI: 6.34e-06 - 0.0163 )
##
## Test for the significance of differences in Objectivity over PA7 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 0.6277, df = 1, p-value = 0.43
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | persuasi
## ---------+-----------
## quasiobj | 0.792280
## | 0.4282
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.000835 (95% CI: 2.17e-06 - 0.00722 )
##
## factor chi2 kruskal_p epsilon2_lci epsilon2 epsilon2_uci
## 1 PA1 0.32 0.57 0 0.000 0.006
## 2 PA2 5.92 < 0.05 0 0.008 0.023
## 3 PA3 0.95 0.33 0 0.001 0.012
## 4 PA5 2.83 0.09 0 0.004 0.025
## 5 PA6 0.75 0.39 0 0.001 0.018
## 6 PA4 1.05 0.31 0 0.001 0.016
## 7 PA7 0.63 0.43 0 0.001 0.007
##
## p < 5e-2 found in: PA2
## p < 1e-2 found in:
## p < 1e-3 found in:
## p < 1e-4 found in:Bindingness
analyze_distributions(broad_data$long, "Bindingness")##
## FALSE TRUE <NA>
## 444 303 6## Saving 7 x 5 in image
## Saving 7 x 5 in image
##
## Test for the significance of differences in Bindingness over PA1 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 389.7403, df = 1, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | FALSE
## ---------+-----------
## TRUE | 19.74184
## | 0.0000*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.518 (95% CI: 0.468 - 0.564 )
##
## Test for the significance of differences in Bindingness over PA2 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 0.0271, df = 1, p-value = 0.87
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | FALSE
## ---------+-----------
## TRUE | 0.164719
## | 0.8692
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 3.61e-05 (95% CI: 2.76e-06 - 0.00677 )
##
## Test for the significance of differences in Bindingness over PA3 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 19.5469, df = 1, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | FALSE
## ---------+-----------
## TRUE | -4.421185
## | 0.0000*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.026 (95% CI: 0.00776 - 0.0531 )
##
## Test for the significance of differences in Bindingness over PA5 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 100.7037, df = 1, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | FALSE
## ---------+-----------
## TRUE | 10.03512
## | 0.0000*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.134 (95% CI: 0.0905 - 0.183 )
##
## Test for the significance of differences in Bindingness over PA6 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 41.3619, df = 1, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | FALSE
## ---------+-----------
## TRUE | -6.431318
## | 0.0000*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.055 (95% CI: 0.026 - 0.0925 )
##
## Test for the significance of differences in Bindingness over PA4 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 31.9676, df = 1, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | FALSE
## ---------+-----------
## TRUE | -5.653993
## | 0.0000*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.0425 (95% CI: 0.0177 - 0.0789 )
##
## Test for the significance of differences in Bindingness over PA7 :
##
## Kruskal-Wallis rank sum test
##
## data: x and group
## Kruskal-Wallis chi-squared = 23.2128, df = 1, p-value = 0
##
##
## Comparison of x by group
## (Bonferroni)
## Col Mean-|
## Row Mean | FALSE
## ---------+-----------
## TRUE | 4.817963
## | 0.0000*
##
## alpha = 0.05
## Reject Ho if p <= alpha
## epsilon2 = 0.0309 (95% CI: 0.0116 - 0.0609 )
##
## factor chi2 kruskal_p epsilon2_lci epsilon2 epsilon2_uci
## 1 PA1 389.74 < 0.0001 0.468 0.518 0.564
## 2 PA2 0.03 0.87 0.000 0.000 0.007
## 3 PA3 19.55 < 0.0001 0.008 0.026 0.053
## 4 PA5 100.70 < 0.0001 0.090 0.134 0.183
## 5 PA6 41.36 < 0.0001 0.026 0.055 0.092
## 6 PA4 31.97 < 0.0001 0.018 0.043 0.079
## 7 PA7 23.21 < 0.0001 0.012 0.031 0.061
##
## p < 5e-2 found in: PA1 PA3 PA5 PA6 PA4 PA7
## p < 1e-2 found in: PA1 PA3 PA5 PA6 PA4 PA7
## p < 1e-3 found in: PA1 PA3 PA5 PA6 PA4 PA7
## p < 1e-4 found in: PA1 PA3 PA5 PA6 PA4 PA7Feature-factor correlations
broad_data_factors_corr <- broad_data$feat_long %>%
group_by(feat, factor) %>%
summarize(correlation = cor(feat_value, factor_score))## `summarise()` has grouped output by 'feat'. You can override using the
## `.groups` argument.broad_data_factors_corr %>%
filter(feat %in% rownames(fa_broad$loadings)) %>%
ggplot(aes(
x = factor,
y = feat,
fill = correlation,
label = round(correlation, 2)
)) +
geom_tile() +
geom_text() +
scale_fill_gradient2(limits = c(-1, 1))
broad_data_factors_corr %>%
filter(!(feat %in% rownames(fa_broad$loadings))) %>%
ggplot(aes(
x = factor,
y = feat,
fill = correlation,
label = round(correlation, 2)
)) +
geom_tile() +
geom_text() +
scale_fill_gradient2(limits = c(-1, 1)) +
labs(x = "factors", y = "variables") +
theme_minimal() +
noto_theme
ggsave("varfactcorr.pdf")## Saving 7 x 9 in imageggsave("varfactcorr.png")## Saving 7 x 9 in image